Dashboard PRO: Series Temporales ARIMA/SARIMA

RMSE

39.0

Error cuadrático medio

MAE

35.0

Error absoluto medio

MAPE

7.4%

Error porcentual

R²

0.725

Coef. determinación

AIC

-406.0

Criterio Akaike

Modelo

SARIMA(1,1,0)(0,1,0)[12]

Mejor por AIC

Modelo	AIC	RMSE
`SARIMA(1,1,0)(0,1,0)`	-359.98	73.2
`SARIMA(1,1,1)(0,1,1)`	-339.17	46.4
`SARIMA(2,1,0)(1,1,0)`	-334.25	54.8
`SARIMA(0,1,1)(0,1,1)`	-340.69	43.1

Autor: @TodoEconometria — Prof. Juan Marcelo Gutiérrez Miranda
Hash ID: 4e8d9b1a5f6e7c3d2b1a0f9e8d7c6b5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c

Referencias académicas:
• Box, G.E.P. & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
• Hyndman, R.J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
• Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press.

Inspiración diseño: OECD Pension Explorer • Portfolio Optimizer (Panel) • Dash Bootstrap Templates

📈 Series Temporales: ARIMA/SARIMA

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